macroeco: reproducible ecological pattern analysis in Python

Lecture

Speaker(s)

Justin was a data science fellow and a postdoctoral scholar in the Energy and Resources Group, where his research focused on the development and application of quantitative approaches for predicting the effects of land use and climate change on biodiversity. He has a particular interest in constraint-based theory and methods, such as maximum information entropy, and is currently working to apply this approach to predict the structure of ecological networks and community dynamics in time. Justin is currently an Assistant Professor in the Department of Biological Sciences at the University of Pittsburgh.

Justin leads the development of the open source Python package macroeco, which supports the development of macroecological methods and their application to conservation. He also has a strong interest in education and training and is a core contributor with the group Software Carpentry, where he develops curriculum and teaches scientific computing workshops.

BIDS fellow Justin Kitzes walks us through his new paper in this interactive abstract.

macroeco is a Python package that supports the analysis of empirical macroecological patterns and the comparison of these patterns to theoretical predictions. Here we describe the use of macroeco and the various functions that it contains. We also highlight a unique high-level interface included with the package, MacroecoDesktop, that allows non-programmers to access the functionality of macroeco. MacroecoDesktop takes simple text-based metadata and parameter files as inputs and generates both tabular and graphical outputs, supporting users in creating reproducible workflows that follow the principles of simplicity, provenance, and automation. Both macroeco and MacroecoDesktop provide case studies for developers of analytically-focused scientific software packages who wish to better support the reproducible use of their tools.